spaCy/spacy/pipeline/defaults/morphologizer_defaults.cfg

[model]
@architectures = "spacy.Tagger.v1"

[model.tok2vec]
@architectures = "spacy.HashCharEmbedCNN.v1"
pretrained_vectors = null
width = 128
depth = 4
embed_size = 7000
window_size = 1
maxout_pieces = 3
nM = 64
nC = 8
dropout = null
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add .cfg to manifest fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build 2020-02-27 17:42:27 +00:00			`[model]`
			`@architectures = "spacy.Tagger.v1"`

			`[model.tok2vec]`
			`@architectures = "spacy.HashCharEmbedCNN.v1"`
			`pretrained_vectors = null`
			`width = 128`
			`depth = 4`
			`embed_size = 7000`
			`window_size = 1`
			`maxout_pieces = 3`
			`nM = 64`
			`nC = 8`
make dropout in embed layers configurable 2020-06-03 09:50:16 +00:00			`dropout = null`